Character-level Convolutional Networks for Text Classification

Part of Advances in Neural Information Processing Systems 28 (NIPS 2015)

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Xiang Zhang, Junbo Zhao, Yann LeCun


This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.